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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Osama Al Qasem
Yarmouk University
Computer Science & IT

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Harnessing deep learning algorithms to predict software refactoring Mamdouh Alenezi; Mohammed Akour; Osama Al Qasem
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.16743

Abstract

During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction
The effectiveness of using deep learning algorithms in predicting students achievements Mohammed Akour; Hiba Al Sghaier; Osama Al Qasem
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp388-394

Abstract

Educational Data Mining (EDM)  research has taking an important place as it helps in exposing useful knowledge from educational data sets to be employed and serve several purposes such as predicting students’ achievements. Predicting student’s achievements might be useful for building and adopting several changes in the educational environments as a re-action in the current educational systems. Most of the existing research have used machine learning to predict students’ achievements by using diverse attributes such as family income, students gender, students absence and level etc.  In this paper, the effort is made to explore the effectiveness of using the deep learning algorithm more precisely CNN to predict students’ achievements which could hlp in predicting if student will be able to finish their degree or not.  The experimental results reveal how the proposed model outperformed the existing approaches in terms of prediction accuracy.
Co-Authors Hiba Al Sghaier Mamdouh Alenezi Mohammed Akour Mohammed Akour